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Aluminum recycling is imperative because of the steady increase in its consumption. Recycling creates a secondary supply stream, lowers production costs and significantly reduces energy use during the production cycle. One of the limiting factors to increased use of scrap in alloys is problematic tramp elements that accumulate in the scrap stream. Currently, alloy producers make use of blending models to assist in choosing from a large number of inputs (scrap sources, primary aluminum, and alloying elements) to manufacture a portfolio of alloys within specification. The goal is to present cost-effective strategies to increase scrap consumption under the applicable context of different operating environments in aluminum production. These blending tools also aim to foster a fundamental shift in decision-making behavior to factor in uncertainties into the scrap management process. Alloys are batched to specification to maximize alloy function which includes a complex set of desired properties. While AA specifications have been put in place to guide batch blending decisions, often maximum constraints result in conservative scrap utilization, thus minimizing the potential for environmental and economic savings. With the wide variety of aluminum alloys available, batching them with the right applications is of the utmost importance, which becomes easier with property based constraints. While the blending models batch the alloys according to the specifications, it is equally necessary to batch them based on their properties to ease the decision making in the scrap management. This trade-off was presented using a linear programming optimization model that tracked four main alloying elements – silicon, magnesium, iron and copper. The optimization model examined the problem of mixing arbitrary quantities of raw materials (scrap aluminum, pure allowing elements and pure aluminum) to produce a set of alloys that met the property based specifications.

For this thesis work, a few selected properties - electrical resistivity, density, elastic modulus and the melting point of aluminum were used as constraints to drive increased scrap use without negatively affecting the performance of the alloys. Results show that increased scrap utilization is possible for a set of specific cases.

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